VERtical Innovation in the Domain of Robotics Enabled by Artificial intelligence Methods

Projektinformationen

VeriDream

ID Finanzhilfevereinbarung: 951992

DOI

10.3030/951992

Projekt abgeschlossen

EK-Unterschriftsdatum 26 Juni 2020

Startdatum 1 Oktober 2020

Enddatum 31 März 2023

Finanziert unter

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Gesamtkosten

€ 1 988 670,00

EU-Beitrag

€ 1 988 670,00

Koordiniert durch

DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EV
Germany

Periodic Reporting for period 2 - VeriDream (VERtical Innovation in the Domain of Robotics Enabled by Artificial intelligence Methods)

Berichtszeitraum: 2021-10-01 bis 2023-03-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Robots act in the real world. When deploying AI methods on robots, the continuous and dynamic nature of the physical world raises many challenges which are not encountered in purely digital domains such as Internet search and social networks. To address these challenges, VeriDream builds on the DREAM [https://cordis.europa.eu/project/id/640891] and RobDream [https://cordis.europa.eu/project/id/645403] research projects to pursue a two-fold innovation strategy for AI in robotics. Its deep innovation strategy will strive to achieve high technological readiness in a set of use cases at a warehouse logistics start-up. Its broad innovation strategy will promote a wider uptake of effective innovation methods in SMEs, thus enhancing the innovation potential of SMEs in AI for robotics, and thus the creation of novel high-quality jobs in Europe.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The main results in the first year have been the detailed definition of ten industrial use cases in which machine learning algorithms will be applied, along with their requirements and key performance indicators. The pragmatic guidelines for the use of the machine learning algorithms have been documented, and four open-source software packages have been published on the European AI On Demand Platform and Ecosystem (AI4EU). Although the pandemic has severely limited the ability of partners to visit each other, first successes have been achieved by applying machine learning to the industrial use cases.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The added value of VeriDream goes beyond the progress in the individual industrial use cases. The use cases themselves are in fact data points for further analysis within VeriDream. Which of the use cases will be success stories? Which of them will lead to only marginal improvement in KPIs, or not at all? Documenting our experiences and lessons learned on the pathway to answering these questions, and sharing them with the broader research and industrial communities is the overarching objective in VeriDream. This will allow other SMEs to understand the advantages and pitfalls of machine learning, thus improving their ability to judge and apply machine learning algorithms for their own use cases. In the long-term, making such knowledge available will make SMEs more competitive, thus ensuring growth and securing jobs.

Overview of the VeriDream project, including the main research and innovation activities